Industrial Vision Systems and Defect Detection

Latest papers

• PCB-MC: Missing Component Analysis in Printed Circuit Boards

  Betsy Villa Brochero, Ian Gibson, Estefania Talavera · 1 October 2026

  Detecting missing components on printed circuit boards (PCBs) differs fundamentally from conventional object detection, as the model must localize components that are not present. We introduce PCB-MC, a curated dataset for missing component detection with footprint level annotations built on top of …

• DCM-SAM: Defect-Conditioned Mixture of LoRA Experts for NPU-Deployed AM Defect Segmentation

  Md Mushfiqur Rahaman, Md Mahedi Hasan, Imtiaz Ahmed, Srinjoy Das · 1 October 2026

  Metal additive manufacturing parts are inspected by X-ray computed tomography, where labelled data is scarce, the pores and inclusions that matter span a few pixels, and inspection must happen at the machine. We present DCM-SAM, a defect-conditioned adaptive mixture of LoRA experts: one frozen Segme…

• LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation

  Shuyang Wu, Yifu Qiu, Ines P. Nearchou, Sandrine Prost, Jonathan A. Fallowfield, Hideki Ueno, Hitoshi Tsuda, David J. Harrison, Hakan Bilen, Timothy J. Kendall · 28 September 2026

  Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at the bag level, hindering the integrated consideration of instance and bag-level information during the analysis. In thi…

• Sonicmesh: Enhancing 3D Human Mesh Reconstruction in Vision-Impaired Environments With Acoustic Signals

  Xiaoxuan Liang, Hong Zhou, Zhaolong Wei, Yansong Li, Shujian Yu, Jeremy Gummeson · 28 September 2026

  3D human mesh reconstruction (HMR) from RGB images often degrades under poor illumination, occlusion, and non-line-of-sight conditions. Acoustic sensing provides complementary spatial cues but suffers from low spatial resolution. We propose SonicMesh, which, to the best of our knowledge, is the firs…

• Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs

  Muhammad Awais, Muhammad Yaseen, Abdul Shakoor, Niaz Ahmed Niaz, Huria Zia, Muhammad Zain Shakoor · 25 September 2026

  Scanning electron microscopy (SEM) is routinely used to characterize the microstructural changes caused by hydrogen embrittlement (HE) in structural steels. Machine learning can automate this characterization, but models are often evaluated using image-level splits. When several images come from the…

• TEEP-RCNN: Texture-Enhanced Edge-aware Perception for Steel Surface Defect Detection via Improved Convolutional Block Attention in Faster R-CNN

  Kirtan Rajesh · 24 September 2026

  Steel surface defect detection is critical for automated industrial quality control but remains challenging due to subtle inter-class texture differences and pronounced class imbalance. We introduce TEEP-RCNN (Texture-Enhanced Edge-aware Perception Region-based CNN), a two-stage detector built on Fa…

• Morphology-Aware Ambiguity Learning for Wafer Defect Decision Support

  Seungjun Chu, Seokhyun Chung · 21 September 2026

  Wafer map defect recognition is commonly formulated as a fixed-taxonomy classification problem that assigns each wafer to a single defect class. However, some wafers exhibit morphologies near class boundaries, for which forcing a single prediction may be less informative than providing plausible dia…

• P$^3$-SAM: SAM with Perceptual Parallel Prompt for Few-Shot Strip Steel Surface Defect Segmentation

  Qian Xu, Hang Xiong, Anpeng Wang, Sam Kwong, Cong Zhang, Runmin Cong · 21 September 2026

  Few-shot semantic segmentation (FSS) of strip steel surface defects (S$^3$D) has posed significant challenges distinct from natural scenes. Unlike natural images, S$^3$D task exhibits unique characteristics including low local contrast, uneven illumination, and complex fine-grained texture patterns.…

• LUMIN: Lightweight Universal Manufacturing Inspection Network for Anomaly Detection

  Pengfei Yang · 7 September 2026

  Industrial anomaly detection faces two engineering bottlenecks: memory bank construction latency and inference efficiency. Traditional sampling algorithms (Farthest Point Sampling, K-Means, etc.) rely on numerous backbone forward passes and iterative distance computations, with construction times ra…

• SafeRestore: Detector-Relative Risk Certificates for Selective Industrial Image Restoration

  Shaoliang Yang, Jun Wang · 4 September 2026

  Industrial inspection pipelines often restore a measured image before a detector acts on it, yet restoration can suppress detector-supported defect structure or create clean-region activations. We formulate restoration as a selective action problem over the measured display, five restored candidates…

• WireSeg-32K: A Physics-Grounded Synthetic Dataset for Wire Instance Segmentation

  Zilin Dai, Lehong Wang, Yi Yang, Xiang Fei · 4 September 2026

  Deformable linear objects such as wires and cables are difficult to segment because they are thin, highly deformable, and frequently self-occluded, while large-scale instance-level annotations are expensive to obtain in real scenes. Existing resources either focus on cable tracing or semantic segmen…

• CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection

  Xinda Yu, Kunxin Zheng, Chunan Yu, Qingbo Song, Hao Xiao, Ying Zang, Jie Liu · 31 August 2026

  Automated detection of surface micro-defects on industrial components, such as copper tubes, is critically important for quality assurance but remains challenging due to the minute scale of anomalies and their visual camouflage against complex backgrounds. These factors lead to weak feature represen…

• Automatic weld seam segmentation for industrial quality control: a comparison of RGB and polarimetric imaging with CNN and transformer architectures

  Simone Garbin, Leonardo Venturoso, Marco Todescato · 27 August 2026

  Visual inspection of welded assemblies remains one of the least automated stages in many industrial production processes, still depending largely on the experience of human operators and thus subject to inter-operator variability; the manufacturing of special-purpose machinery cabins, the setting of…

• Trustworthy Visual Quality Inspection under Data Scarcity in Manufacturing

  Panagiotis Sapoutzoglou, Jessy Ribaira, Martin Kanounnikoff, Bas Tijsma, Christian Gei{\ss}, Maria Pateraki · 25 August 2026

  Automated visual inspection in manufacturing aims to replace slow and inconsistent manual checks, but its economic value depends on whether its decisions can be trusted enough to automate routine inspection while reserving human expertise for ambiguous cases. In production-line settings, defective s…

• AI Visual Inspection for Garment Production

  Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong · 25 August 2026

  The garment manufacturing industry is under increasing pressure to improve product quality, reduce costs, and accelerate digital transformation toward Industry 4.0. One of the most challenging quality-control activities is sewing-line inspection, where defects such as broken stitches and skipped sti…

• Continuity-Driven Representation Learning for Industrial Defect Detection

  Minjong Kim, Hyun Jun Kim, Jeongrae Kim, Heeseung Shin, Changwon Lim · 19 August 2026

  Industrial defect detection differs from natural-image object detection because inspection images are captured under controlled conditions and contain large normal-dominant regions with repetitive structures. Defects therefore appear as localized disruptions of otherwise predictable patterns, while …

• Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection

  Jie Deng · 18 August 2026

  Studies of industrial visual inspection commonly report the area under the receiver operating characteristic curve (AUROC) and the overlap between anomaly maps and defect masks. Neither measure specifies the false-alarm rate at a selected threshold, while recurrent defect locations and mask geometry…

• Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

  Yicheng Kang, Yuling Jiao, Xin Geng, Mahesh Nagarajan · 17 August 2026

  Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management. In this paper, we introduce MODERN, a deep …

• Low Cost Two-Stage Fabric Defect Detection at the Edge

  Rasel Hossen, Diptajoy Mistry, Mosaddek Hossain Kamal · 14 August 2026

  Fabric inspection in the garment industries of low-income economies remains largely manual, and commercial vision systems are priced beyond most small and medium mills. Because defects are sparse under controlled production, a natural response is a cascade: screen every frame with a cheap anomaly de…

• Precise Top-Layer Fabric Segmentation for Fabric Destacking with Edge- and Shape-Aware Deep Networks

  Wenbo Dong, Dipankar Bhattacharya, Akinari Kobayashi, Akira Seino, Fuyuki Tokuda, Xuzhao Huang, Kai Tang, Norman C. Tien, Kazuhiro Kosuge · 12 August 2026

  Fabric destacking requires precise segmentation of the topmost fabric layer, a task complicated by subtle fabric boundaries and high visual similarity between fabric layers. Existing semantic and edge-based segmentation approaches often struggle with these complexities, limiting the performance of r…

• RobustDefect-LLM: Explainable and Robustness-Aware Industrial Surface Defect Classification with Decision Support and AI-Assisted Reporting

  Nazlıcan Düşünmez, Halûk Gümüşkaya · 11 August 2026

  This paper presents RobustDefect-LLM, an industrial surface-defect inspection framework integrating deep-learning classification, operator-facing visual evidence, confidence-aware decision support, controlled AI-assisted reporting, traceable storage, and mobile interaction in a unified quality-contr…

• LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing

  Wenbo Sui, Daniel Lichau, Harold Phelippeau, Zhao Liu · 11 August 2026

  Multimodal industrial anomaly detection has largely focused on discrete products using strongly correlated RGB and 3D observations, leaving continuous process manufacturing and weakly correlated sensing modalities underexplored. We introduce LIBAD, the first multimodal anomaly detection benchmark fo…

• HyperFake: Hyperspectral Reconstruction and Attention-Guided Analysis for Advanced Deepfake Detection

  Pavan C Shekar, Pawan Soni, Vivek Kanhangad · 11 August 2026

  Deepfakes pose a significant threat to digital media security, with current detection methods struggling to generalize across different manipulation techniques and datasets. While recent approaches combine CNN-based architectures with Vision Transformers or leverage multi-modal learning, they remain…

• Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning

  Aryuemaan Kumar Chowdhury · 3 August 2026

  Here is the plain text version optimized for arXiv's submission form. Custom macros (like \CV and \SI) have been converted to standard text/math so they render correctly on the webpage: Evaluating the fatigue life of structural steels conventionally requires mechanical testing lasting tens to hundre…

• ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

  Paul Julius K\"uhn, Saptarshi Neil Sinha, Tiago Kleist, Richard Hoffmann, Arjan kuijper, Michael Weinmann · 30 July 2026

  While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this task challenging. This paper presents a procedural rendering pipeline that generates large-scale annotated synthetic train…